Near surface air temperature ( \(\:{T}_{a}\) ) represents a vital climatic parameter essential for comprehending and modeling complex surface phenomena, as well as for elucidating the dynamics of hydrological processes. This study integrated \(\:{T}_{a}\) data acquired using smartphone weather application and portable anemometer, and land surface temperature ( \(\:{T}_{s}\) ) to predict diurnal near-surface hourly air temperature for data-scarce area by fitting these data into the Gaussian function method. The spatial continuity of \(\:{T}_{a}\) was computed from the air temperature data collected using the smartphone weather app. The linear scaling approach was applied to minimize bias in the \(\:{T}_{a}\) predicted using smartphone weather app. Thermal infrared (TIR-1) band of Landsat-8/9 Operational Land Imager (OLI) was used to derive \(\:{T}_{s}\) . The computed diurnal hourly \(\:{T}_{a}\) revealed a polynomial relationship with time. Comparative analysis results of the interpolated and measured hourly \(\:{T}_{a}\) revealed the measured hourly \(\:{T}_{a}\:\) being often higher than the predicted hourly \(\:{T}_{a}\:\) across the surveyed hours. The linear scaling bias correction technique corrected the interpolated hourly \(\:{T}_{a}\) to the values closer to the measured values. Moreover, \(\:{T}_{s}\) was found to be generally higher than both measured and interpolated \(\:{T}_{a}\) , though \(\:{T}_{s}\) lower than \(\:{T}_{a}\) was noted in the vicinity of water bodies, which points to the inability of the interpolated \(\:{T}_{a}\) to account for the influence of water. The linear regression analysis results of the relationship between the measured \(\:{T}_{a}\) and remote sensing-based \(\:\:{T}_{s}\) was established, with r2 of 0.69. The diurnal hourly \(\:{T}_{a}\) pattern extrapolated by the Gaussian function also revealed a polynomial relationship between the simulated hourly \(\:{T}_{a}\) and time. Analysis of the root mean square error (RMSE) and observation standard deviation ratio (RSR) values revealed good performance of the Gaussian fitting technique in simulating diurnal hourly \(\:{T}_{a}\) in the study area, with RMSE and RSR values of 1.41 and 0.55 respectively. Most importantly, the simulated \(\:{T}_{a}\) based on \(\:{T}_{s}\) accounted for the influence of land use, such as water bodies. The results of this study reveals that the Gaussian fitting model can predict diurnal hourly \(\:{T}_{a}\) based on \(\:{T}_{s}\:\) data derived from Landsat-8 OLI TIR-1 channel. Ultimately, this research highlighted the continuous role of geospatial technology in addressing environmental and climate change issues.
